1 This project was supported by Grant No. 2009-DG-BX-K021 awarded by the Bureau of Justice Assistance. The Bureau of Justice Assistance is a component of the Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, and the Office for Victims of Crime. Points of view or opinions in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice.

Charles M. Katz, Arizona State University (ASU) ▪ Andrew Fox, University of Missouri – Kansas City ▪ Michael White, ASU ▪ David Choate, ASU

October 28, 2013

The Role of Social Network Analysis in Intelligence-Led Policing

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Definition: Intelligence-led policing “A managerial philosophy where data analysis and crime intelligence are pivotal to an objective, decision-making framework that facilitates crime and problem reduction, disruption and prevention through both strategic management and effective enforcement strategies that target prolific and serious offenders.”

Source: Ratcliffe, J. (2008). Intelligence-Led Policing, Willan Publishing: p. 89.

• Strategic decisions • Intelligence/data

driven • Focus on problem

reduction • Target prolific

offenders

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Policing paradigms

Crime events Offenders Narrow

Broad Community

policing

POP

Compstat ILP

Operational focus

Pro

blem

focu

s P

robl

em fo

cus

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The Utility of SNA in ILP ILP • Using intelligence to

address criminal groups and prolific offenders.

• It often focuses on social relationships

SNA • Maps social

relationships • Identifies

group/organizational structures

• Identifies those at the center of criminal groups

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Sociogram

Node: individuals, gangs, businesses

Edge or tie: type of relationship (associate, enemy, alliance)

Directed tie

Undirected tie

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This is not just link analysis! 1. Degree Centrality – Simply the number of

ties a node has in the network.

2. Betweenness Centrality – Those who are the intersection on many paths between others.

3. Eigenvector Centrality – Those who are connected to many connected people

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Example: The 9-11 Hijacker Network

SOURCE: Valdis Krebs http://www.orgnet.com/

The 19 terrorists were ALL within two steps of the two original suspects identified in 2000

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Is SNA possible with police data? Strengths • Already collected • Easily accessible • Structured,

relational, and temporal data is plentiful

Weaknesses • Incompleteness

• Inaccuracies

• Inconsistencies

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Advantages of Using SNA • Layout optimization

– No lines on top of each other, clear layout – Space on the page to equal social distance

• Identifying key players

– Centrality as a measure of importance

• Free software (Pajek and Excel)

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Stop & Think • What kind of intelligence is available to you?

– Crime reports – Field interview cards – GMIC’s – Court transcripts – National Integrated Ballistic Information Network

(NIBIN) – Telephone records – Jail/correctional visits – Free talks

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The Glendale Police Department Pilot Study • ASU collected relational data from 2006-2010

– GMIC – FI Cards – Merged with criminal history data

• Major findings – Intelligence was fairly reliable – Consistently collected – Time consuming to pull – Data management systems not optimally designed

for SNA – SNA has strong utility for ILP

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Major findings, cont. • There was not one large cohesive gang; the

network consisted of many smaller connected groups.

• Gang members from different cliques were found to be in the same social network.

• Hybrid gangs were the most criminally involved.

• Betweenness centrality was more important with respect to criminal involvement.

• Gang membership * cohesion = more crime.

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Examples of clique affiliations in 2007

Key: Varrio Sixty First = Red; West Side Grandel = Blue; Varrio Clavalito Park = Green

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Betweenness Centrality

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Betweenness Centrality Top Betweenness No Yes

Age 22.86 21.89

Gender Male 82.5 95.7 Female 17.5 4.3

Race Asian 0.0 0.0 Indian 1.1 0.0 Hispanic 89.8 100.0 White 6.8 0.0 Black 1.7 0.0 Other 0.6 0.0

Entered network as: Gang member 41.2 * 83.3 Gang associate 51.3 16.7 Associate of associate 7.5 0.0

Number of Arrests Part 1 Violent 0.49 * 0.92 Part 1 Property 0.96 1.17 Part 2 Drug 0.93 1.33 Part 2 Sex 0.04 0.00 Part 2 Misc 3.57 * 5.33 Total number of arrests 5.29 * 7.79

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Operation Jenga: Proof of concept for the PPD

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What do we need to know at this point?

• How hard is it to get to the data? • Does it produce ties we otherwise would not

know of? • Do subgroups exist? • What is the structure of the network? • What roles do members in the network play?

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How we started Operation Jenga • Step 0, Anna Bella, recommended by a couple of

detectives. – Suspect of money laundering – Has been kidnapped twice ($1M each) – Ex-husband was in a Mexican Cartel – She owns several check cashing businesses and a tax service,

notary business. • Data we used:

– FI’s – DR’s – PPD only

• Went back 3 years • 2 steps

Ms. A. (Start of network), 1V/1W, Kidnapped 2x, $1M ransom

Mr. Unknown, 2S/1IL

Mr. B, 22 yrs old, 4S/4IL

Ms. Y (unknown), Prostitute 1S/4W/3V

Mr. F (unknown), 4A/3V

Mr. P, 1S/2IL

Mr. C, 6IL/1S/IA/1C

Person X 5S/2IL/1W

Mr. H, (boyfriend), 2A/2V/1C

1 lead resulted in 320 people

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What else do we now know? • SNA can work with PPD data • Labor intensive data collection • One lead resulted in 320 relationships after 2

steps • 50% of network reachable through 2 people. • Key players are not necessarily the most

criminally involved

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Challenges in using police data • Labor-intensive

– Manual look-up and verification of individuals – Manual build of edge-lists

• Quality concerns – CAD/RMS systems without a reliable unique identifier (with

look-up capability) for every individual in the system contributes to errors in both inclusion and exclusion of individuals in the network

• Timeliness – Manual processes reduce tactical utility – CAD/RMS able to automate edge-list builds would provide

near real-time analyses.

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So what? What are we going to do with this stuff? • Degree centrality- number of ties a node has in the

network – Not the most strategic targets – Could be important in collecting information on a network

(e.g., informants, free talks, etc.) • Betweenness centrality - intersection of many paths

between others. – Strategic targets for disrupting a network – Ideal contagion agents for a deterrence message – Call-ins would be best directed at these individuals

• Eigenvector centrality - connected to many connected people – Collective accountability – Pulling levers strategy

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Stop & Think • If you found these same findings in your

community what would you do?

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Early Offender Network Model Each dot represents a documented gang member and their first tier relationships

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Early offender network • 360 members in group • 202 in largest connected group • 60 currently were on probation / parole • 32 pending cases were in Jackson County

processes • 126 members had active warrants • 22 warrants were Felony

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Pilot Group High Betweenness and Active Warrants

Betweenness Centrality – Those who are the intersection on many paths between others

Red indicates wanted parties

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Training • Finding the right crime analysts • Giving them time and space to learn • Need to fully understand PD data systems and

how to extract large amounts of data from those systems

• Need to understand the concepts, not just the technique

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Analysis • Software

– Pajek: Free, Windows-based – UCINet: Free, Windows-based

• Resources – Wasserman & Faust (1994), Social Network

Analysis: Methods and Applications – Training seminars – Local university – CNA

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Questions, comments? Charles M. Katz

ckatz@asu.edu

Andrew Fox foxan@umkc.edu

Michael White Michael.D.White.1@asu.edu

David Choate David.Choate@asu.edu